scholarly journals Learning the architectural features that predict functional similarity of neural networks

2020 ◽  
Author(s):  
Adam Haber ◽  
Elad Schneidman
Keyword(s):  
Neural Networks ◽  
Architectural Design ◽  
Neural Circuits ◽  
High Accuracy ◽  
Structure And Function ◽  
Functional Similarity ◽  
Architectural Features ◽  
Wide Range ◽  
And Function ◽  
Sparse Set

ABSTRACTThe mapping of the wiring diagrams of neural circuits promises to allow us to link structure and function of neural networks. Current approaches to analyzing connectomes rely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and networks, and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph theory metrics convey little information about the similarity of networks’ responses. Instead, we learn a functional metric between networks based on their synaptic differences, and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features - the sum of synaptic inputs that each neuron receives and the sum of each neuron’s synaptic outputs - predicts the functional similarity of networks of up to 100 cells, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks, which conform with experimental evidence of homeostatic mechanisms.

scholarly journals Inter-species comparison of the orientation algorithm directing larval chemotaxis in the genus Drosophila

2021 ◽  
Author(s):  
Elie Fink ◽  
Matthieu Louis
Keyword(s):  
Neural Circuits ◽  
Rapid Evolution ◽  
Structure And Function ◽  
Olfactory Behavior ◽  
Species Comparison ◽  
Closely Related Species ◽  
Genetic Studies ◽  
Phenotypic Differences ◽  
Peripheral Olfactory System ◽  
And Function

Animals differ in their appearances and behaviors. While many genetic studies have addressed the origins of phenotypic differences between fly species, we are still lacking a quantitative assessment of the variability in the way different fly species behave. We tackled this question in one of the most robust behaviors displayed by Drosophila: chemotaxis. At the larval stage, Drosophila melanogaster navigate odor gradients by combining four sensorimotor routines in a multilayered algorithm: a modulation of the overall locomotor speed and turn rate; a bias in turning during down-gradient motion; a bias in turning toward the gradient; the local curl of trajectories toward the gradient ("weathervaning"). Using high-resolution tracking and behavioral quantification, we characterized the olfactory behavior of eight closely related species of the Drosophila group in response to 19 ecologically-relevant odors. Significant changes are observed in the receptive field of each species, which is consistent with the rapid evolution of the peripheral olfactory system. Our results reveal substantial inter-species variability in the algorithms directing larval chemotaxis. While the basic sensorimotor routines are shared, their parametric arrangements can vary dramatically across species. The present analysis sets the stage for deciphering the evolutionary relationships between the structure and function of neural circuits directing orientation behaviors in Drosophila.

scholarly journals Recovery of dynamics and function in spiking neural networks by closed-loop control

2015 ◽  
Author(s):  
Ioannis Vlachos ◽  
Taskin Deniz ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Brain Activity ◽  
Open Loop ◽  
Spiking Neural Networks ◽  
Delayed Feedback Control ◽  
Loop Control ◽  
Wide Range ◽  
Underlying Network ◽  
And Function

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks. Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC besides steering the system back to a healthy state, it also recovers the computations performed by the underlying network. Finally, using our theory we isolate the role of single neuron and synapse properties in determining the stability of the closed-loop system.

scholarly journals Regulation of OmpA Translation and Shigella dysenteriae Virulence by an RNA Thermometer

2019 ◽  
Vol 88 (3) ◽  
Author(s):  
Erin R. Murphy ◽  
Johanna Roßmanith ◽  
Jacob Sieg ◽  
Megan E. Fris ◽  
Hebaallaha Hussein ◽  
...  
Keyword(s):  
In Silico ◽  
Bacterial Species ◽  
Regulation Of Gene Expression ◽  
Structure And Function ◽  
Shigella Dysenteriae ◽  
Content Type ◽  
Bacterial Physiology ◽  
Wide Range ◽  
Rna Thermometer ◽  
And Function

ABSTRACT RNA thermometers are cis-acting riboregulators that mediate the posttranscriptional regulation of gene expression in response to environmental temperature. Such regulation is conferred by temperature-responsive structural changes within the RNA thermometer that directly result in differential ribosomal binding to the regulated transcript. The significance of RNA thermometers in controlling bacterial physiology and pathogenesis is becoming increasingly clear. This study combines in silico, molecular genetics, and biochemical analyses to characterize both the structure and function of a newly identified RNA thermometer within the ompA transcript of Shigella dysenteriae. First identified by in silico structural predictions, genetic analyses have demonstrated that the ompA RNA thermometer is a functional riboregulator sufficient to confer posttranscriptional temperature-dependent regulation, with optimal expression observed at the host-associated temperature of 37°C. Structural studies and ribosomal binding analyses have revealed both increased exposure of the ribosomal binding site and increased ribosomal binding to the ompA transcript at permissive temperatures. The introduction of site-specific mutations predicted to alter the temperature responsiveness of the ompA RNA thermometer has predictable consequences for both the structure and function of the regulatory element. Finally, in vitro tissue culture-based analyses implicate the ompA RNA thermometer as a bona fide S. dysenteriae virulence factor in this bacterial pathogen. Given that ompA is highly conserved among Gram-negative pathogens, these studies not only provide insight into the significance of riboregulation in controlling Shigella virulence, but they also have the potential to facilitate further understanding of the physiology and/or pathogenesis of a wide range of bacterial species.

State-of-the-Art Neural Networks Applications in Biology

2013 ◽  
Vol 2 (4) ◽  
pp. 63-85 ◽  
Author(s):  
Arianna Filntisi ◽  
Nikitas Papangelopoulos ◽  
Elena Bencurova ◽  
Ioannis Kasampalidis ◽  
George Matsopoulos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Game Theory ◽  
State Of The Art ◽  
Disease Diagnosis ◽  
General Term ◽  
Structure And Function ◽  
Computational Method ◽  
Processing Abilities ◽  
Central Nervous Systems ◽  
And Function

Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combination of informatics, biology and medicine. This article is an effort to investigate recent advances in the area of bioinformatical applications of ANNs, with emphasis in disease diagnosis, genetics, proteomics, and chemoinformatics. The combination of neural networks and game theory in some of these application is also discussed.

Commentary on “Reflections on culture, structure and function of an intensive support service centred on positive behavioural support”

2016 ◽  
Vol 21 (4) ◽  
pp. 212-219 ◽  
Author(s):  
Sandy Toogood
Keyword(s):  
Support Service ◽  
Structure And Function ◽  
Content Type ◽  
Behavioural Support ◽  
Wide Range ◽  
Service Models ◽  
History Of ◽  
Positive Behaviour Support ◽  
The Uk ◽  
And Function

Purpose The purpose of this paper is to provide a commentary on Patterson and Berry’s paper “Reflections on culture, structure and function of an intensive support service centred on positive behavioural support”. Design/methodology/approach This paper reviews key ideas presented in Patterson and Berry’s article relative to the recent history of service delivery in the UK and the growing interest being shown in positive behaviour support. Findings Patterson and Berry’s article adds to a modest literature on specialist support services and should stimulate further descriptions of service models and the concepts underpinning them. Originality/value The literature on specialist support service models is limited and this addition should be relevant to a wide range of clinicians, consumers and commissioners.

scholarly journals Predicting atmospheric optical properties for radiative transfer computations using neural networks

2020 ◽  
Author(s):  
Menno Veerman ◽  
Robert Pincus ◽  
Caspar van Leeuwen ◽  
Damian Podareanu ◽  
Robin Stoffer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Radiative Transfer ◽  
Grid Cell ◽  
High Accuracy ◽  
Average Error ◽  
Multilayer Perceptrons ◽  
Shallow Convection ◽  
Wide Range ◽  
The Neural Networks

<p>A fast and accurate treatment of radiation in meteorological models is essential for high quality simulations of the atmosphere. Despite our good understanding of the processes governing the transfer of radiation, full radiative transfer solvers are computationally extremely expensive. In this study, we use machine learning to accelerate the optical properties calculations of the Rapid Radiative Transfer Models for General circulation model applications - Parallel (RRTMGP). These optical properties control the absorption, scattering and emission of radiation within each grid cell. We train multiple neural networks that get as input the pressure, temperature and concentrations of water vapour and ozone of each grid cell and together predict all 224 or 256 quadrature points of each optical property. All networks are multilayer perceptrons and we test various network sizes to assess the trade-off between the accuracy of a neural network and its computational costs. We train two different sets of neural networks. The first set (generic) is trained for a wide range of atmospheric conditions, based on the profiles chosen by the Radiative Forcing Model Intercomparison Project (RFMIP). The second set (case-specific) is trained only for the range in temperature, pressure and moisture found in one large-eddy simulation based on a case with shallow convection over a vegetated surface. This case-specific set is used to explore the possible performance gains of case-specific tuning.</p><p>Most neural networks are able to predict the optical properties with high accuracy. Using a network with 2 hidden layers of 64 neurons, predicted optical depths in the longwave spectrum are highly accurate (R<sup>2 </sup>> 0.99). Similar accuracies are achieved for the other optical properties. Subsequently, we take a set of 100 atmospheric profiles and calculate profiles of longwave and shortwave radiative fluxes based on the optical properties predicted by the neural networks. Compared to fluxes based on the optical properties computed by RRTMGP, the downwelling longwave fluxes have errors within 0.5 W m<sup>-2</sup> (<1%) and an average error of -0.011 W m<sup>-2</sup> at the surface. The downwelling shortwave fluxes have an average error of -0.0013 W m<sup>-2</sup> at the surface. Using Intel’s Math Kernel Library’s (MKL) BLAS routines to accelerate matrix multiplications, our implementation of the neural networks in RRTMGP is about 4 times faster than the original optical properties calculations. It can thus be concluded that neural networks are able to emulate the calculation of optical properties with high accuracy and computational speed.</p>

scholarly journals Structure and function of the immune system in the spleen

2019 ◽  
Vol 4 (33) ◽  
pp. eaau6085 ◽  
Author(s):  
Steven M. Lewis ◽  
Adam Williams ◽  
Stephanie C. Eisenbarth
Keyword(s):  
Cell Types ◽  
Structure And Function ◽  
Lymphoid Organ ◽  
The Body ◽  
Human Spleen ◽  
Cell Clearance ◽  
Wide Range ◽  
Abnormal Cells ◽  
Cell Organization ◽  
And Function

The spleen is the largest secondary lymphoid organ in the body and, as such, hosts a wide range of immunologic functions alongside its roles in hematopoiesis and red blood cell clearance. The physical organization of the spleen allows it to filter blood of pathogens and abnormal cells and facilitate low-probability interactions between antigen-presenting cells (APCs) and cognate lymphocytes. APCs specific to the spleen regulate the T and B cell response to these antigenic targets in the blood. This review will focus on cell types, cell organization, and immunologic functions specific to the spleen and how these affect initiation of adaptive immunity to systemic blood-borne antigens. Potential differences in structure and function between mouse and human spleen will also be discussed.

scholarly journals Biosynthesis of Polyketides in Streptomyces

2019 ◽  
Vol 7 (5) ◽  
pp. 124 ◽  
Author(s):  
Chandra Risdian ◽  
Tjandrawati Mozef ◽  
Joachim Wink
Keyword(s):  
Secondary Metabolites ◽  
Structure And Function ◽  
Polyketide Synthases ◽  
Type I ◽  
Type Ii ◽  
Filamentous Form ◽  
Gram Positive Bacteria ◽  
Wide Range ◽  
Different Types ◽  
And Function

Polyketides are a large group of secondary metabolites that have notable variety in their structure and function. Polyketides exhibit a wide range of bioactivities such as antibacterial, antifungal, anticancer, antiviral, immune-suppressing, anti-cholesterol, and anti-inflammatory activity. Naturally, they are found in bacteria, fungi, plants, protists, insects, mollusks, and sponges. Streptomyces is a genus of Gram-positive bacteria that has a filamentous form like fungi. This genus is best known as one of the polyketides producers. Some examples of polyketides produced by Streptomyces are rapamycin, oleandomycin, actinorhodin, daunorubicin, and caprazamycin. Biosynthesis of polyketides involves a group of enzyme activities called polyketide synthases (PKSs). There are three types of PKSs (type I, type II, and type III) in Streptomyces responsible for producing polyketides. This paper focuses on the biosynthesis of polyketides in Streptomyces with three structurally-different types of PKSs.

Dance’s Resource-fullness

Valuing Dance ◽  
2019 ◽  
pp. 23-50
Author(s):  
Susan Leigh Foster
Keyword(s):  
Structure And Function ◽  
Virtual Power ◽  
Vast Number ◽  
Hypothetical Construct ◽  
Wide Range ◽  
And Function

Chapter 1 introduces a hypothetical construct called “dance’s resource-fullness”—a set of conjectured but unverifiable capacities dance might have that could be tapped for exchange either as commodity or as gift. These capacities consist of the ability to bring people into relation, to generate as well as expend energy, and to adapt to a wide range of contexts and needs. In support of these conjectures about dance, the chapter utilizes a methodology of list-making and draws upon diverse studies of dance including philosophical, sociological, anthropological, and neurophysiological inquiries. Dance’s capacity to bring people into relation is assessed in terms of the ways it summons participants, how it develops the space in which it occurs, and the types of subjecthood it constructs. Dance’s facility at generating energy is explained through recourse to theories of dance as play, as synchrony, as bodily becoming, as virtual power, and as mobilization. Dance’s facility at adapting to an array of contexts is demonstrated through the vast number of typologies of dance that have been proposed concerning its structure and function.

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